We consider agents in a multi-agent system, each equipped with a Bayesian network model (BN) of its environment. We want the agents to reach consensus on one compromise network, which may not be identical to a single one of the BNs initially held by the agents, but rather a combination of aspects from each BN. The task can be characterized as the need for agents to agree on a specific state (a BN) of a variable with an enormous state space (all possible BNs). The grandness of the task is reduced by the fact that BNs are composed of local relationships, and it should therefore be possible to reach the compromise by gradually agreeing on parts of it. In the metaphor of the variable, the agents should be able to agree on successively smaller subsets of the enormous state space. However, these same local relationship can interact, and understanding the extent to which partial agreements affect the possible final compromise is a highly complex task. In this work we suggest using formal argumentation as the reasoning mechanism for agents solving this task, and suggest an open-ended agora approach that ensures agents high quality compromises in an anytime fashion.